Bibliographic record
Abstract
Within the United States of America, French is of importance in only two areas, Louisiana and New England, the latter often being referred to as the Québec d'en bas for its high number of French-Canadian immigrants. Among the six states that constitute New England, Massachusetts is the one that attracted most of them, Québécois as well as Acadiens. Despite the high number of citizens of French-Canadian origin and the proximity to Canada, French has been losing ground as a langue du foyer in all of New England but especially in the southern part. This sociolinguistic study concentrates on the process of language decay among the French-Canadian population of Massachusetts. Based on a corpus consisting of 87qualitative interviews and a quantitative questionnaire survey of 392 questionnaires in 7 areas (covering the centers of French-Canadian immigration throughout Massachusetts),this study approaches the topic in a new, broader angle by encompassing the following aspects: ananalysis of U.S. Census data on ancestry and language use, an overview of the history of French-Canadian presence in Massachusetts, various specificities of the varieties of Canadian French spoken there, as well as ananalysis of the extralinguistic factors, such as the heterogeneity of the French-speaking population, and the intralinguistic consequences, such as unskilled code-switching,of language decay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".